ATLAS Searches with Unconventional Signatures

Many theories beyond the Standard Model have been proposed to address several of its shortcomings. Some of these beyond-the-Standard-Model extensions predict new particles or interactions directly accessible at the Large Hadron Collider, but which would leave unconventional signatures in the ATLAS detector. These unconventional signatures require special techniques and reconstruction algorithms that, once developed, enable analysers to perform unique searches for new physics. These proceedings cover several such searches using the Run-2 dataset.

Authors

Publication Details

Published
2024-12-27
DOI
https://doi.org/10.22323/1.469.0140
Primary Topic
Machine Learning and Algorithms
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

ATLAS Searches with Unconventional Signatures

M. Ressegotti
Machine Learning and Algorithms
article

ATLAS Searches with Unconventional Signatures

M. Ressegotti
article en

Abstract

Many theories beyond the Standard Model have been proposed to address several of its shortcomings. Some of these beyond-the-Standard-Model extensions predict new particles or interactions directly accessible at the Large Hadron Collider, but which would leave unconventional signatures in the ATLAS detector. These unconventional signatures require special techniques and reconstruction algorithms that, once developed, enable analysers to perform unique searches for new physics. These proceedings cover several such searches using the Run-2 dataset.

Openalex Percentile: Top 78%
Machine Learning and Algorithms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.